A note on tests of sphericity and cross-sectional dependence for fixed effects panel model

This paper theoretically explains why bias correction appears in two statistics recently developed by Baltagi et al. (2011, 2012), which are designed to test the sphericity and cross-sectional dependence of the errors in the fixed effects panel model respectively. Our explanation shows that the bias correction is in fact avoidable, which is demonstrated by two corresponding statistics that are newly constructed in this paper. Simulation suggests that our statistics perform as well as the two in Baltagi et al. (2011, 2012). In addition, according to the theories underlying our explanation, we extend a new sphericity test proposed by Fisher et al. (2010) to the fixed effects model. Simulation finds that the test behaves well only if both the cross-sectional and the time series dimension are large.     

Semiparametric binary random effects models: Estimating two types of drinking behavior

I propose a new estimator for cross-section semiparametric regressions containing unobserved binary random effects and apply it to alcohol consumption. The random effects (health consciousness) explain a significant proportion of otherwise unexplained variation in alcohol consumption. Education positively correlates with health consciousness.     

Testing for serial correlation and random effects in a two-way error component regression model

In this paper, we test the existence of serial correlation and random effects in a two-way error component regression model with panel data. Under moment conditions alone, we suggest several easily implemented tests based on the parameter estimators for artificial autoregressions modeled by the differences in residuals. Under the null hypotheses, the tests for serial correlation are two-sided and asymptotically chi-square distributed, whereas those for random effects are one-sided, and are asymptotically standard normally distributed variables. Moreover, these methods can also be used similarly to construct tests for both serial correlation and individual effects jointly, whether or not time effects are present. The proposed tests are able to detect local alternatives that are distinct from the null at the parametric rate. Monte Carlo simulations and real data applications are carried out for purposes of illustration.     

Instrumental variable estimation of a spatial autoregressive panel model with random effects

This paper extends the instrumental variable estimators of Kelejian and Prucha (1998) and Lee (2003) proposed for the cross-sectional spatial autoregressive model to the random effects spatial autoregressive panel data model. It also suggests an extension of the Baltagi (1981) error component 2SLS estimator to this spatial panel model.     

On the distributional effects of income in an aggregate consumption relation

Abstract .  In this paper we analyse the influence of characteristics of the income distribution in modelling aggregate consumption expenditure. We model the aggregate consumption relation of a heterogeneous population, using a statistical distributional approach of aggregation, and apply it to UK-Family Expenditure Survey data. A bootstrap test based on a non-parametric estimation methodology, which accounts for the presence of continuous and discrete variables, suggests that the mean and the dispersion of the income distribution significantly influence aggregate consumption expenditure. Also, the parameters of the aggregate relation are time varying. These findings have implications for constructing empirically sound models of aggregate consumption expenditure.     

Modified Quasi‐Likelihood Ratio Test for Regime Switching

In this paper we propose a modified quasi‐likelihood ratio test of the null hypothesis of one regime against the alternative of two regimes in Markov regime‐switching models. The asymptotic distribution of the proposed test statistic is a simple function of Gaussian random variables, and the inference is no more complicated than in the standard case. Our simulations show that the proposed test has good finite sample size and power that are comparable to the quasi‐likelihood ratio test of Cho and White. We apply our test to stock returns and Japanese policy functions.     

Threshold effects in panel data stochastic frontier models of dairy production in Canada

One of the most enduring problems in econometrics is how to properly account for heterogeneity among firms. Threshold regression models are intuitively appealing methods to deal with this issue. We consider a fixed-effect panel data stochastic frontier model (Schmidt and Sickles, 1984; Martin-Marcos and Suarez-Galvez, 2000) and, relying on Hansen (1999, 2000a), we propose an estimator that accommodates multiple thresholds. Our model assumes absence of any unmeasured time invariant heterogeneity across firms as in Greene (2005, p. 277). Slope and threshold parameters can be estimated using a within estimator combined with a grid search over the threshold parameters. Testing for threshold effects is problematic because threshold parameters are not identified under the null hypothesis, a case of the so-called Davies' problem. We apply the bootstrap procedure proposed by Hansen (1999, 2000a) to test for the presence of thresholds. An asymptotic confidence set for the threshold parameter can be obtained by inverting an LR test, using the distribution result presented in Hansen (1999, 2000a). Our empirical application features a panel of Quebec dairy farms. We use farm size as the threshold variable. The presence of a trend in the specification matters for the determination of the number of thresholds. Technical efficiency scores and rankings of farms estimated from competing model specifications are highly correlated and do not vary significantly across groups of farm sizes defined by the threshold parameter values.     

Identification and estimation of thresholds in the fixed effects ordered logit model

This paper proposes a new estimator for the fixed effects ordered logit model. In contrast to existing methods, the new procedure allows estimating the thresholds. The empirical relevance and simplicity of implementation is illustrated in an application on the effect of unemployment on life satisfaction.     

Exploring the effects of aggregation error in the estimation of consumer demand elasticities

Errors introduced by using aggregate data in estimating a consumer demand model have long been a concern. We study the effects of such errors on elasticity estimates derived from AIDS and QUAIDS models. Based on a survey of published articles, a generic parameterization of the income distribution, and the range of Gini coefficients reported for 28 OECD countries, we generate and analyze a large number of “observations” on the differences between elasticities calculated at the aggregate level and those calculated at the micro level. We suggest a procedure for evaluating the likely range of aggregation error when a model is estimated with aggregate data.     

A simple method for estimating unconditional heterogeneity distributions in correlated random effects models

I propose a general, simple approach to recovering an unconditional heterogeneity distribution when a conditional distribution has been estimated. The approach can be applied to cross section models and panel data models-both static and dynamic-with unobserved heterogeneity.     

Small-sample inference with spatial HAC estimators

This paper examines the small-sample performance of spatial HAC (SHAC) estimators of the standard errors on parameters. We find that, in small to moderately-sized datasets, the use of HAC estimators may be recommended only with a relatively large degree of cross-sectional interdependence.     

Gravity model estimation: fixed effects vs. random intercept Poisson pseudo-maximum likelihood

The standard Anderson and van Wincoop gravity model has typically been estimated using a fixed-effects approach. However, a fixed-effects approach has a major drawback: it does not allow for the estimation of exporter- and importer-invariant variables. Thus, at least in a cross-sectional context, economically relevant variables such as exporter and importer gross domestic product are disregarded. We propose a random intercept model to address this gap. For large datasets, this approach not only provides identical estimates like a fixed-effects approach, but also allows for the estimation of exporter- and importer-invariant variables.     

Testing for structural breaks with local smoothers: A simulation study

By means of an extensive Monte Carlo simulation study based on the design of Chen and Hong (2012) we compare the performance of the tests they proposed for parameter stability with the linearity test of Li et al. (2002) and the functional form test of Li and Wang (1998). We find that the test of Li et al. (2002) test adapted to testing for parameter stability performs favorably well in terms of size and equally well in terms of power compared with the others, whereas the test by Li and Wang has no power.     

Predicting within country household food expenditure variation using international cross-section estimates

International cross-section demand systems have not been tested on their ability to accurately predict within country consumption expenditures. This paper reports on such a validation exercise for the case of food budget share predictions across expenditure percentiles within Bangladesh.     

Specification tests and tests for overidentifying restrictions in panel data models with selection

Standard tests are generally not applicable in panel data models with selection. The paper shows how the Hausman specification test and the Sargan-Hansen test for overidentifying restrictions can be generalized to panel data models with unobserved heterogeneity and sample selection.     

Model averaging with covariates that are missing completely at random

Missing data is a common problem in economics studies. We propose using Mallows model averaging (MMA) to deal with this problem, which has an important advantage over its competitors in that it asymptotically achieves the lowest possible squared error. A simulation study in comparison with existing methods strongly favors the MMA estimator.     

A simple unit root test against asymmetric STAR nonlinearity with an application to real exchange rates in Nordic countries

Existing tests of the unit root hypothesis against the alternative hypothesis of exponential smooth transition autoregressive (ESTAR) nonlinearity implicitly assume symmetry under the alternative. This paper proposes a simple unit root test against the alternative of symmetric or asymmetric ESTAR nonlinearity. In the event that the unit root hypothesis is rejected, a simple test of symmetric versus asymmetric ESTAR nonlinearity is also proposed. The asymptotic distributions of the test statistics are straightforward to establish and finite-sample performance is studied with Monte Carlo simulations. An empirical application involving the real exchange rates of four Nordic countries against the U.S. dollar illustrates the usefulness of the new tests.     

GARCH with omitted persistent covariate

This paper analyzes the effect of omitting a persistent covariate in the GARCH-X model. In particular, we show that if the relevant persistent covariate is omitted and the usual GARCH(1,1) model is fitted, the model will be estimated approximately as an IGARCH model. This may well explain the ubiquitous evidence of the IGARCH in empirical volatility analysis.     

Testing the growth effects of structural change

Against the backdrop of Baumol's model of ‘unbalanced growth’, a recent strand of literature has presented models that manage to reconcile structural change with Kaldor's ‘stylized fact’ of the relative constancy of per-capita real GDP growth. Another strand of literature goes beyond this, arguing that the expenditure shifts toward Baumol's ‘stagnant’ sector stimulate rather than dampen long-term economic growth because of the human capital-accumulating nature of major ‘stagnant’ services (like health care and education). This paper tests the relationship between structural change and economic growth empirically by means of a Granger-causality analysis of a panel of 18 OECD countries.